code: def _derive_all_distances(X, window_sizes, shapelets, lengths, fit):
      """Derive the distances between all the shapelets and the time series."""
      n_samples, n_timestamps = X.shape
      if fit:
          distances = _derive_all_squared_distances_fit(
              X,
              n_samples,
              n_timestamps,
              window_sizes,
              shapelets,
              lengths,
          )
          return np.sqrt(np.asarray(distances)).T
      else:
          (
              distances,
              permutation,
          ) = _derive_all_squared_distances_transform(
              X,
              n_samples,
              n_timestamps,
              window_sizes,
              shapelets,
              lengths,
          )
          # Compute the inverse permutation of the indices
          permutation = np.concatenate(permutation)
          inverse_perm = np.arange(permutation.size)[
              np.argsort(permutation)
          ]
  
          return np.sqrt(np.asarray(distances))[inverse_perm].T
  
imports: from itertools import chain
  from joblib import delayed, Parallel
  from numba import njit, prange
  from numba.typed import List
  import numpy
  from numpy.lib.stride_tricks import as_strided
  from sklearn.base import BaseEstimator
  from sklearn.feature_selection import f_classif, mutual_info_classif
  from sklearn.utils.validation import check_array, check_is_fitted, check_random_state, check_X_y
  from ..base import UnivariateTransformerMixin
  from ..utils.utils import _windowed_view
label_desc: Use image node_id 5 for calling a global function with example usage: _derive_all_distances(X, window_sizes, shapelets, lengths, fit) and returns: np, np